SIGMA: Spatial Integrated Matching Association algorithm for logo detection

In this paper, we adopt the integration model of spatial feature correlations to order the indexing and matching features, and address the computational ineffectiveness and inefficiency of local features based logo detection methods. We propose a Spatial InteGrated Matching Association algorithm (SIGMA) for logo detection in natural scene that contains extremely variances in viewpoints, illuminations and occlusions. Our SIGMA algorithm consists of two phases: the Spatial InteGrated (SIG) phase and the Matching Association (MA) phase. The SIG phase integrates spatial correlations in feature representation, while the MA phase improves the matching performance by ordering an optimized matching sequence. We have collected a logo dataset containing 2,400 photos with 12 logo categories from Flickr, and experimental results demonstrate that the performance of proposed approach outperforms the state-of-the-art approaches on the dataset.